{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 使用plot绘制折线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [i for i in range(1, 11)]\n",
    "y = [i**2 for i in range(1, 11)]\n",
    "plt.plot(x, y)\n",
    "plt.title(\"y=x^2\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.tick_params(axis=\"both\",labelsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 使用scatter绘制散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5j0WSNMBYn7qpqheB+4DNfU3XAHdW1z7gaeCsZp0Dzc+DwE5g44Q1S5LGsGTQJ5lNsq65fizwfuCJvm5/Dbyv6XMycCbwVJLjkhzfLD8O+ADw6PTKlyQtZZRP3ZwC3Jpkhu4Lw1eq6u4k2wCqajvwGeAPkuwFAnyqql5I8i5gZ/c9Wo4CvlxVX1+JByJJGmyUT908Apw3YPn2nusH6B6t9/d5CjhnwholSRPwm7GS1HIGvSS1nEEvSS1n0EtSyxn0ktRyBr0ktZxBL0ktZ9BLUssZ9JLUcga9JLWcQS9JLWfQS1LLGfSS1HIGvSS1nEEvSS1n0EtSy40yleCbk/x5koeTPJbktwf0eVuS/9XT55qets1JnkyyL8kN034AkqTFjXJE/wpwUVWdA5wLbE5yQV+fa4HvNH3+BfB7SY5pph+8CdgCbACuTrJhatVLkpa0ZNBX18vNzaObS/V3A45Pd3LYtwLfBw4BG4F9VfVUVb0K3AFcPq3iJUlLG+kcfZKZJHuAg8A3q+rBvi5fBN4NHAD2AtdX1evAqcCzPf32N8sGbWNrkvkk8wsLC2M+DEnSMCMFfVW9VlXnAnPAxiRn93X5ILAHeAfd0ztfTHICkEF3N2QbO6qqU1Wd2dnZkR+AJGlxY33qpqpeBO4DNvc1XQPc2Zzm2Qc8DZxF9wj+tJ5+c3SP+iVJq2SUT93MJlnXXD8WeD/wRF+3vwbe1/Q5GTgTeAp4CDgjyelJjgGuAu6aXvmSpKUcNUKfU4Bbm0/QvAn4SlXdnWQbQFVtBz4D/EGSvXRP13yqql4ASHIdcA8wA9xSVY+twOOQJA2RqoGnzNdUp9Op+fn5tS5Dko4YSXZVVWdQm9+MlaSWM+glqeUMeklqOYNeklrusHwzNskC8FfLXP0k4IUpljMt1jUe6xqPdY2njXW9s6oGftv0sAz6SSSZH/bO81qyrvFY13isazz/0Ory1I0ktZxBL0kt18ag37HWBQxhXeOxrvFY13j+QdXVunP0kqSf1sYjeklSD4NeklrusA76JLckOZjk0SHtJybZmeSRZgLzs3vaBk5KnuTnk3wzyXebnyeuVl1JTkvyf5I83kyifn3POr+V5Lkke5rLxatVV9P2TJK9zbbne5av5f46s2d/7EnyUpKPN20T7a/FnouePknyhWYMPZLk/J62FRlfk9S1kuNrCvtrRcbXhPtrxcbXGLWdleTbSV5J8om+tumNsao6bC/AhcD5wKND2j8HfLq5fhbwreb6DPA94F3AMcDDwIam7T8ANzTXbwD+/SrWdQpwfnP9eOAve+r6LeATa7G/mtvPACcNWGfN9ldfnxngb+h+KWTi/bXYc9HT52Lga3T/9fYFwIMrPb4mrGvFxtckda3k+Jq0rpUaX2PU9o+Afwz8u97tTXuMHdZH9FV1P92JxofZAHyr6fsEsD7diU8Wm5T8cuDW5vqtwBWrVVdVPV9Vf9Es/yHwOEPm0F2OCfbXYtZsf/X1eR/wvapa7jem+2sa5bm4HLituh4A1iU5hRUcX5PUtZLja8L9tZg12199faY6vkatraoOVtVDwI/7Vp/qGDusg34EDwNXAiTZCLyT7nSFi01KfnJVPQ/dJ4LuK+pq1fUTSdYD5wG9E61f1/xpecu4f8JOoa4CvpFkV5KtPescFvuL7uxk/71v2VT215DnAoaPo1UZX8uoa6l112p/wSqMr0n2Fys4vpaobZipjrEjPeg/C5yYZA/wMWA3cIgxJiVf5boASPJW4I+Bj1fVS83iLwG/RHdy9eeB31vlujZV1fnAFuDaJBeuwPaXUxfpTkN5GfA/etaZyv4a8lz8pHnAKrXI8qlZZl2LrbuW+wtWeHxNuL9WbHyNUNvQ1QYsW/YYG2UqwcNWs9Ouge4bLnQnJX8aeAvDJyX/2zf+zG3+fDu4inWR5Gi6T/ofVtWdPev87RvXk/wX4O7VrKuqDjQ/DybZSfdPx/tZ4/3V2AL8Re8+msb+GvZc9Bg2uf0xQ5bDFPbXBHWt6PiapK6VHF+T1NVYkfE1Ym3DLFbz2PvsiD6iT7KueTUG+ChwfxMai01Kfhfwa831XwP+52rV1YTY7wOPV9V/7Fun95zhrwIDP6GyQnUdl+T4ps9xwAd6tr9m+6uny9X0/Vk96f5a7LnocRfw4XRdAPyg+VN5xcbXJHWt5PiasK4VG18TPo9vmPr4GqO2YaY7xpZ6t3YtL3R3/vN036jYD3wE2AZsa9r/KfBd4AngTuDEnnUvpvsu9/eAf9Oz/BfovvH33ebnz69WXcA/o/vn1yPAnuZycdP234C9TdtdwCmrWNe76J4nfxh47HDZX03bW4C/A97Wd58T7a9hz0VfXQFuasbQXqCz0uNrkrpWcnxNWNeKja8pPI8rMr7GqO3tdH8nXgJebK6fMO0x5r9AkKSWO6JP3UiSlmbQS1LLGfSS1HIGvSS1nEEvSS1n0EtSyxn0ktRy/x9xFGwadV7s4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(2, 4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+wUW4W5Xk1cA3gU9U1R+m3c9WSvJ+4ExVPTrtXrbRbuCtwJeq6ibgT0zon+o7UXeM+SBwA/A64PIkH5puV7NvlsN96xfh3oGSvIJBsPer6sFp97MNbgE+kOQXDA69/X2Sr0+3pS13CjhVVS/9q+wBBmHfqncDP6+qtar6L+BB4B1T7mk7PZdkD0A3npnEh85yuG/9Itw7TJIwOA57oqo+P+1+tkNVfaaqrquqBQZ/xt+rqqZndVX1a+DZJG/oSgeAp6bY0lZbBW5OMtf9HT9Aw79A3sQx4FD3+BDw0CQ+dMvWUN1q27YI985yC/Bh4MdJftTVPtutV6u2fAzodxOXnwEfmXI/W6aqHknyAPAYgzPCHqfR2xAkuQ94F3BVklPA3cA9wP1J7mDwg+62iezL2w9IUntm+bCMJOk8DHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUoP8BNcxn1cTITVYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [i for i in range(11)]\n",
    "y = [i**2 for i in range(11)]\n",
    "plt.scatter(x, y, c=\"red\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 进阶版（绘制渐变颜色）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [i for i in range(11)]\n",
    "y = [i**2 for i in range(11)]\n",
    "# c用于传递颜色的值，在使用渐变时，需要将渐变的的值传入，这里传入y值\n",
    "# 同时开始设置渐变 cmap=plt.cm.Reds (具体颜色具体选择-这里选择Reds)\n",
    "plt.scatter(x, y, c=y, cmap=plt.cm.Reds)\n",
    "# plt.scatter(x, y, c=y, cmap=plt.cm.Reds, edgecolor='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. 使用scatter创建一个小小样例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bean\\AppData\\Roaming\\Python\\Python36\\site-packages\\ipykernel_launcher.py:39: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "C:\\Users\\Bean\\AppData\\Roaming\\Python\\Python36\\site-packages\\ipykernel_launcher.py:40: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from random import choice\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "\n",
    "class RandWalk():\n",
    "    def __init__(self,poit=5000):\n",
    "        self.poit = poit\n",
    "        self.x = [0]\n",
    "        self.y = [0]\n",
    "        \n",
    "    def _walk(self):\n",
    "        while len(self.x) <= self.poit:\n",
    "            temp01 = choice([-1,1])\n",
    "            num = choice([i for i in range(1,5)])\n",
    "            x_temp = temp01 * num\n",
    "            temp02 = choice([-1,1])\n",
    "            num = choice([i for i in range(1,5)])\n",
    "            y_temp = temp02 * num\n",
    "            \n",
    "            if x_temp == 0 and y_temp == 0:\n",
    "                continue\n",
    "            \n",
    "            x_next = self.x[-1] + x_temp\n",
    "            y_next = self.y[-1] + y_temp\n",
    "            self.x.append(x_next)\n",
    "            self.y.append(y_next)\n",
    "     \n",
    "    @property\n",
    "    def get_poit(self):\n",
    "        self._walk()\n",
    "        a, b = self.x, self.y\n",
    "        return a, b\n",
    "\n",
    "\n",
    "rw = RandWalk()\n",
    "x, y = rw.get_poit\n",
    "plt.scatter(x,y,c=y, cmap=plt.cm.Blues, s=10)\n",
    "# 用于隐藏坐标轴\n",
    "plt.axes().get_xaxis().set_visible(False)\n",
    "plt.axes().get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. 使用bar绘制柱形图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [i for i in range(1, 11)]\n",
    "y = [i**2 for i in range(1, 11)]\n",
    "plt.bar(x, y)\n",
    "# 给坐标轴加入值\n",
    "for a,b in zip(x,y):\n",
    "    plt.text(a,b, b,ha = 'center',va = 'bottom')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. 使用pie绘制圆饼图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [i for i in range(1, 6)]\n",
    "y = [i**2 for i in range(1, 6)]\n",
    "plt.pie(y,labels=labels)\n",
    "plt.axis('equal')\n",
    "# 加入标签\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
